CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity.

Important Links
💻 Website	Check out the SDV Website for more information about our overall synthetic data ecosystem.
📙 Blog	A deeper look at open source, synthetic data creation and evaluation.
📖 Documentation	Quickstarts, User and Development Guides, and API Reference.
Repository	The link to the Github Repository of this library.
⌨️ Development Status	This software is in its Pre-Alpha stage.
Community	Join our Slack Workspace for announcements and discussions.

Currently, this library implements the CTGAN and TVAE models described in the Modeling Tabular data using Conditional GAN paper, presented at the 2019 NeurIPS conference.

⚠️ If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. ⚠️

The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. See the SDV documentation to get started.

Alternatively, you can also install and use CTGAN directly, as a standalone library:

Using pip:

pip install ctgan

Using conda:

conda install -c pytorch -c conda-forge ctgan

When using the CTGAN library directly, you may need to manually preprocess your data into the correct format, for example:

• Continuous data must be represented as floats
• Discrete data must be represented as ints or strings
• The data should not contain any missing values

In this example we load the Adult Census Dataset* which is a built-in demo dataset. We use CTGAN to learn from the real data and then generate some synthetic data.

from ctgan import CTGAN
from ctgan import load_demo

real_data = load_demo()

# Names of the columns that are discrete
discrete_columns = [
    'workclass',
    'education',
    'marital-status',
    'occupation',
    'relationship',
    'race',
    'sex',
    'native-country',
    'income'
]

ctgan = CTGAN(epochs=10)
ctgan.fit(real_data, discrete_columns)

# Create synthetic data
synthetic_data = ctgan.sample(1000)

*For more information about the dataset see: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Join our Slack channel to discuss more about CTGAN and synthetic data. If you find a bug or have a feature request, you can also open an issue on our GitHub.

Interested in contributing to CTGAN? Read our Contribution Guide to get started.

If you use CTGAN, please cite the following work:

Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni. Modeling Tabular data using Conditional GAN. NeurIPS, 2019.

@inproceedings{ctgan,
  title={Modeling Tabular data using Conditional GAN},
  author={Xu, Lei and Skoularidou, Maria and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan},
  booktitle={Advances in Neural Information Processing Systems},
  year={2019}
}

Please note that these projects are external to the SDV Ecosystem. They are not affiliated with or maintained by DataCebo.

• R Interface for CTGAN: A wrapper around CTGAN that brings the functionalities to R users. More details can be found in the corresponding repository: https://github.com/kasaai/ctgan
• CTGAN Server CLI: A package to easily deploy CTGAN onto a remote server. Created by Timothy Pillow @oregonpillow at: https://github.com/oregonpillow/ctgan-server-cli

The Synthetic Data Vault Project was first created at MIT's Data to AI Lab in 2016. After 4 years of research and traction with enterprise, we created DataCebo in 2020 with the goal of growing the project. Today, DataCebo is the proud developer of SDV, the largest ecosystem for synthetic data generation & evaluation. It is home to multiple libraries that support synthetic data, including:

• 🔄 Data discovery & transformation. Reverse the transforms to reproduce realistic data.
• 🧠 Multiple machine learning models -- ranging from Copulas to Deep Learning -- to create tabular, multi table and time series data.
• 📊 Measuring quality and privacy of synthetic data, and comparing different synthetic data generation models.

Get started using the SDV package -- a fully integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries for specific needs.